Sparse data interpolation using the geodesic distance affinity space
In this paper, we adapt the geodesic distance-based recursive filter to the sparse data interpolation problem. The proposed technique is general and can be easily applied to any kind of sparse data. We demonstrate the superiority over other interpolation techniques in three experiments for qualitative and quantitative evaluation. In addition, we compare our method with the popular interpolation algorithm presented in the EpicFlow optical flow paper that is intuitively motivated by a similar geodesic distance principle. The comparison shows that our algorithm is more accurate and considerably faster than the EpicFlow interpolation technique.
Code (0)
등록된 구현이 없습니다.
Tasks
Optical Flow EstimationSimilar Papers 제목 키워드 기반
EpicFlow: Edge-Preserving Interpolation of Correspondences for Optical Flow
We propose a novel approach for optical flow estimation , targeted at large displacements with significant oc-clusions. It consists of two steps: i) dense matching by edge-preserving interpolation from a sparse set of ma…
Optical Flow EstimationEfficient, sparse representation of manifold distance matrices for classical scaling
Geodesic distance matrices can reveal shape properties that are largely invariant to non-rigid deformations, and thus are often used to analyze and represent 3-D shapes. However, these matrices grow quadratically with th…
Short and Straight: Geodesics on Differentiable Manifolds
Manifolds discovered by machine learning models provide a compact representation of the underlying data. Geodesics on these manifolds define locally length-minimising curves and provide a notion of distance, which are ke…
validInterpoNet, A brain inspired neural network for optical flow dense interpolation
Sparse-to-dense interpolation for optical flow is a fundamental phase in the pipeline of most of the leading optical flow estimation algorithms. The current state-of-the-art method for interpolation, EpicFlow, is a local…
Optical Flow EstimationManifold-augmented Eikonal Equations: Geodesic Distances and Flows on Differentiable Manifolds
Manifolds discovered by machine learning models provide a compact representation of the underlying data. Geodesics on these manifolds define locally length-minimising curves and provide a notion of distance, which are ke…